Device impairment detection

ABSTRACT

Examples described herein involve detecting known impairments or other known conditions using a neural network. An example implementation receives a response matrix that represents, in respective dimensions, responses of a given playback device under respective iterations of a sound captured by a recording device, the iterations including first iterations with respective impairments to the recording device and second iterations without the respective impairments to the recording device. The implementation determines principle components representing the axes of greatest variance in the response matrix, a principle-component matrix that represents a given set of the principle components, and a teaching matrix by projecting the principle-component onto the response matrix. The implementation trains a neural network that includes an output layer comprising a neuron for each of the respective impairments by iteratively providing vectors of the teaching matrix to the neural network and stores the trained neural network on a computing system.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 to, and is acontinuation of, U.S. non-provisional patent application Ser. No.14/864,533, filed on Sep. 24, 2015, entitled “Device ImpairmentDetection,” which is incorporated herein by reference in its entirety.

U.S. non-provisional patent application Ser. No. 14/864,533 claimspriority to U.S. provisional Patent Application No. 62/220,158, filed onSep. 17, 2015, entitled “Detecting impairments of a microphonemeasurement or audio recording,” which is also incorporated herein byreference in its entirety.

FIELD OF THE DISCLOSURE

The disclosure is related to consumer goods and, more particularly, tomethods, systems, products, features, services, and other elementsdirected to media playback or some aspect thereof.

BACKGROUND

Options for accessing and listening to digital audio in an out-loudsetting were limited until in 2003, when SONOS, Inc. filed for one ofits first patent applications, entitled “Method for Synchronizing AudioPlayback between Multiple Networked Devices,” and began offering a mediaplayback system for sale in 2005. The Sonos Wireless HiFi System enablespeople to experience music from a plethora of sources via one or morenetworked playback devices. Through a software control applicationinstalled on a smartphone, tablet, or computer, one can play what he orshe wants in any room that has a networked playback device.Additionally, using the controller, for example, different songs can bestreamed to each room with a playback device, rooms can be groupedtogether for synchronous playback, or the same song can be heard in allrooms synchronously.

Given the ever growing interest in digital media, there continues to bea need to develop consumer-accessible technologies to further enhancethe listening experience.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, aspects, and advantages of the presently disclosed technologymay be better understood with regard to the following description,appended claims, and accompanying drawings where:

FIG. 1 shows an example media playback system configuration in whichcertain embodiments may be practiced;

FIG. 2 shows a functional block diagram of an example playback device;

FIG. 3 shows a functional block diagram of an example control device;

FIG. 4 shows an example controller interface;

FIG. 5 shows an example computing system;

FIG. 6 shows a flow diagram of an example technique to train a neuralnetwork to detect known impairments or conditions;

FIG. 7 shows an illustrative neuron of a neural network;

FIG. 8 shows another illustrative neuron of a neural network;

FIG. 9 shows an example neural network;

FIG. 10 shows a flow diagram of an example technique to detect knownimpairments or conditions using a neural network;

FIG. 11 shows an illustrative playback device calibration userinterface; and

FIG. 12 shows an illustrative playback device calibration userinterface.

The drawings are for the purpose of illustrating example embodiments,but it is understood that the inventions are not limited to thearrangements and instrumentality shown in the drawings.

DETAILED DESCRIPTION

I. Overview

Embodiments described herein may involve, inter alia, detecting one ormore impairments that might affect calibration of one or more playbackdevices of a media playback system. Some calibration procedurescontemplated herein involve a recording device (e.g., a smartphone orother portable computing device) detecting and analyzing sound (e.g.,one or more calibration sounds) emitted by one or more playback devicesin a given listening environment. By analyzing sound that has propagatedthrough the listening environment, a media playback system may determinehow the listening environment is affecting sound emitted by the playbackdevice(s) and perhaps also how to address the effect of the listeningenvironment on the playback device(s). For example, the media playbacksystem may determine a calibration profile that adjusts the outputfrequency of a playback device to offset the acoustics of theenvironment.

However, in some circumstances, one or more impairments may interferewith aspects of a calibration procedure (e.g., the emitting ofcalibration sound(s) by one or more playback devices or the recording ofthose calibration sounds by the recording device). Possible impairmentsthat might be detected by the disclosed techniques include, but are notlimited to object(s) or obstruction(s) at least partially covering orotherwise obstructing or affecting sound emitted by one or more speakersof a playback device or other device as the sound is being used to emitaudio in connection with a playback device calibration procedure.Further possible impairments that could be detected by the disclosedtechniques include, but are not limited to object(s) or obstruction(s)at least partially covering or otherwise obstructing or affecting soundas the sound is detected by one or more microphones of a recordingdevice, playback device, or other device that is being used to recordaudio in connection with a playback device calibration procedure.

Such objects and/or obstructions may include, for example, (i) a user'sfinger at least partially covering (typically inadvertently) one or moremicrophones that are being to detect, monitor, and/or record sound aspart of the calibration procedure, (ii) a smartphone case at leastpartially covering or enclosing one or more smartphone microphones thatare being used to detect, monitor, and/or record sound as part of thecalibration procedure, (iii) lint, dust, or other debris on or in one ormore microphones being used to detect, monitor, and/or record sound inconnection with the calibration procedure (generally referred to as“flint”), (iv) objects that are on a table, shelf, or wall near thedevice comprising one or more of the microphones that are being used toemit and/or record sound in connection with the calibration procedure,and/or (v) other object(s) and/or (vi) other object(s) and/orobstruction(s) that tend to affect sound waves received by the one ormore microphones being used to detect, monitor, and/or record sound inconnection with the calibration procedure.

After detecting an impairment, some embodiments may further include oneor more of (a) indicating to a user visually (via a control device orplayback device), or audibly (via controller or audio playback device),that an impairment has been detected by visual or other means and/or (b)correcting the calibration to offset the detected impairment. In someembodiments, the action taken may vary depending on the type ofimpairment detected. For example, for impairments that can be corrected,the techniques disclosed herein may correct or compensate for theimpairment. For other impairments (e.g., impairments that cannot bereadily offset), the systems and methods disclosed herein may includethe control device or playback device notifying a user (via a visible,audible, physical (e.g., vibration), or other notification) of thedetected (or suspected) impairment so that the user can take action tocorrect or remove the impairment. The notification may also includeinstructions or suggestions for correcting the detected (or suspected)impairment.

Example embodiments contemplated herein utilize a neural network todetect impairments. An example neural network includes a plurality ofneurons corresponding to an impairment (or lack thereof). Each neuronimplements a transfer function that produces an output indicative of a“firing” of the neuron when an input vector characteristic of animpaired system is provided as input to the neuron. In this way, firingof a neuron indicates that a particular impairment that corresponds tothe firing neuron is present. In some cases, wherein multipleimpairments are present, a given input vector may cause multiple neuronsto fire thereby indicating the present of two or more particularimpairments.

As noted above, one or more input vectors may be provided as input tothe neural network so as to detect presence of impairments. Such inputvectors may be particular vectors derived to represent, in acomputationally-efficient manner, the complex system of playbackdevice(s) and recording device(s) operating in a listening area withunknown characteristics.

To obtain such an input vector, a recording device (e.g., a smartphone)may record audio (e.g., a test tone) emitted by one or more playbackdevices in a given listening area. A processing device (referred togenerally herein as a network device or a control device and which mayinclude the recording device) may generate a “response” indicative ofthis system (e.g. a power spectral density). This response may berepresented as a vector (e.g., a response vector) that includescomponents which represent the response of the system in respectivefrequency ranges. For instance, an example response vector may include8000 components, each component representing power density of therecorded audio within a respective range of frequencies.

The input vector may be determined from the response vector byprojecting the input vector onto a principle component matrix thatrepresents variance caused by the known impairments that the neuralnetwork is trained to detect. During training of the neural network, arecording device may record audio (e.g., a test tone) emitted by aplayback device both with and without each known impairment present togenerate a matrix of responses under a range of iterations. From thismatrix of responses, a processing device may extract principlecomponents, which are eigenvectors representing the axes of greatestvariance in the matrix of responses. These principle component vectorsare combined into a matrix (i.e., the principle component matrix).

While the techniques described here may have application to detectingimpairments of a microphone or of a device, the techniques describedherein may also have application to detecting conditions generally. Forinstance, the techniques described herein may be employed by devices ofa media playback system (e.g., playback and/or control devices) todetect conditions present in an operating environment (e.g., thepresence of conditions that affect operation of the media playbacksystem), or the nature of the operating environment itself (e.g.,indoors/outdoors, size of room, type of room, furnishings and/orfinishes of the room, among other examples). Yet further, the techniquesdescribed herein may be applied by computing devices generally to detectvarious conditions (e.g., physical conditions) based on sensor data(e.g. radio frequency data, such as IEEE 802.11 WiFi data). Further,such techniques may be used in combination with other detectiontechniques (e.g., conventional detecting techniques based on sensordata) to increase confidence in the detection of known conditions.

As indicated above, embodiments described herein involve identifying oneor more impairments affecting calibration of one or more playbackdevices. In one aspect, a network device is provided. The network deviceincludes a microphone, one or more processors, and a tangible datastorage having stored therein instructions executable by the one or moreprocessors to cause the network device to perform operations. Theoperations include receiving data indicating a response of one or moreplayback devices captured by a given microphone. The operations furtherinclude determining an input vector by projecting a response vector thatrepresents the response of the one or more playback devices onto aprinciple component matrix representing variance caused by one or morecalibration impairments. The operations also include providing thedetermined input vector to a neural network that includes an outputlayer comprising neurons that correspond to respective calibrationimpairments. The operations include detecting that the input vectorcaused one or more neurons of the neural network to fire such that theneural network indicates that one or more particular calibrationimpairments are affecting the microphone and adjusting a calibration ofthe one or more playback devices to offset the one or more particularcalibration impairments.

In another aspect, a method is provided. The method involves receivingdata indicating a response of one or more playback devices captured by agiven microphone. The method further involves determining an inputvector by projecting a response vector that represents the response ofthe one or more playback devices onto a principle component matrixrepresenting variance caused by one or more calibration impairments. Themethod also involves providing the determined input vector to a neuralnetwork that includes an output layer comprising neurons that correspondto respective calibration impairments. The method involves detectingthat the input vector caused one or more neurons of the neural networkto fire such that the neural network indicates that one or moreparticular calibration impairments are affecting the microphone andadjusting a calibration of the one or more playback devices to offsetthe one or more particular calibration impairments.

In another aspect, a non-transitory computer-readable medium isprovided. The non-transitory computer-readable medium has stored thereoninstructions executable by a computing device to perform operations. Theoperations include receiving data indicating a response of one or moreplayback devices captured by a given microphone. The operations furtherinclude determining an input vector by projecting a response vector thatrepresents the response of the one or more playback devices onto aprinciple component matrix representing variance caused by one or morecalibration impairments. The operations also include providing thedetermined input vector to a neural network that includes an outputlayer comprising neurons that correspond to respective calibrationimpairments. The operations include detecting that the input vectorcaused one or more neurons of the neural network to fire such that theneural network indicates that one or more particular calibrationimpairments are affecting the microphone and adjusting a calibration ofthe one or more playback devices to offset the one or more particularcalibration impairments.

Further example embodiments described herein involve training a neuralnetwork to detect impairments or other conditions present in a system.In an aspect, a computing system is provided. The computing systemincludes one or more processors, and a tangible data storage havingstored therein instructions executable by the one or more processors tocause the computing system to perform operations. The operations includereceiving a response matrix that represents, in respective dimensions,responses of a given playback device under respective iterations of asound captured by a recording device, the iterations including firstiterations with respective impairments to the recording device andsecond iterations without the respective impairments to the recordingdevice. The operations further include determining principle componentsrepresenting the axes of greatest variance in the response matrix, theprinciple components comprising respective eigenvectors that include acomponent for each of the respective iterations. The operations alsoinclude determining a principle-component matrix that represents a givenset of the principle components. The operations include determining ateaching matrix by projecting the principle-component onto the responsematrix. The operations also include training a neural network thatincludes an output layer comprising a neuron for each of the respectiveimpairments by iteratively providing vectors of the training matrix tothe neural network and storing the trained neural network.

In an aspect, a method is provided. The method involves receiving aresponse matrix that represents, in respective dimensions, responses ofa given playback device under respective iterations of a sound capturedby a recording device, the iterations including first iterations withrespective impairments to the recording device and second iterationswithout the respective impairments to the recording device. The methodfurther involves determining principle components representing the axesof greatest variance in the response matrix, the principle componentscomprising respective eigenvectors that include a component for each ofthe respective iterations. The method also involves determining aprinciple-component matrix that represents a given set of the principlecomponents. The method involves determining a teaching matrix byprojecting the principle-component onto the response matrix. The methodalso involves training a neural network that includes an output layercomprising a neuron for each of the respective impairments byiteratively providing vectors of the training matrix to the neuralnetwork and storing the trained neural network.

In another aspect, a non-transitory computer-readable medium isprovided. The non-transitory computer-readable medium has stored thereoninstructions executable by a computing device to perform operations. Theoperations include receiving a response matrix that represents, inrespective dimensions, responses of a given playback device underrespective iterations of a sound captured by a recording device, theiterations including first iterations with respective impairments to therecording device and second iterations without the respectiveimpairments to the recording device. The operations further includedetermining principle components representing the axes of greatestvariance in the response matrix, the principle components comprisingrespective eigenvectors that include a component for each of therespective iterations. The operations also include determining aprinciple-component matrix that represents a given set of the principlecomponents. The operations include determining a teaching matrix byprojecting the principle-component onto the response matrix. Theoperations also include training a neural network that includes anoutput layer comprising a neuron for each of the respective impairmentsby iteratively providing vectors of the training matrix to the neuralnetwork and storing the trained neural network.

While some examples described herein may refer to functions performed bygiven actors such as “users” and/or other entities, it should beunderstood that this is for purposes of explanation only. The claimsshould not be interpreted to require action by any such example actorunless explicitly required by the language of the claims themselves. Itwill be understood by one of ordinary skill in the art that thisdisclosure includes numerous other embodiments.

II. Example Operating Environment

FIG. 1 shows an example configuration of a media playback system 100 inwhich one or more embodiments disclosed herein may be practiced orimplemented. The media playback system 100 as shown is associated withan example home environment having several rooms and spaces, such as forexample, a master bedroom, an office, a dining room, and a living room.As shown in the example of FIG. 1, the media playback system 100includes playback devices 102-124, control devices 126 and 128, and awired or wireless network router 130.

Further discussions relating to the different components of the examplemedia playback system 100 and how the different components may interactto provide a user with a media experience may be found in the followingsections. While discussions herein may generally refer to the examplemedia playback system 100, technologies described herein are not limitedto applications within, among other things, the home environment asshown in FIG. 1. For instance, the technologies described herein may beuseful in environments where multi-zone audio may be desired, such as,for example, a commercial setting like a restaurant, mall or airport, avehicle like a sports utility vehicle (SUV), bus or car, a ship or boat,an airplane, and so on.

a. Example Playback Devices

FIG. 2 shows a functional block diagram of an example playback device200 that may be configured to be one or more of the playback devices102-124 of the media playback system 100 of FIG. 1. The playback device200 may include a processor 202, software components 204, memory 206,audio processing components 208, audio amplifier(s) 210, speaker(s) 212,microphone(s) 220, and a network interface 214 including wirelessinterface(s) 216 and wired interface(s) 218. In one case, the playbackdevice 200 may not include the speaker(s) 212, but rather a speakerinterface for connecting the playback device 200 to external speakers.In another case, the playback device 200 may include neither thespeaker(s) 212 nor the audio amplifier(s) 210, but rather an audiointerface for connecting the playback device 200 to an external audioamplifier or audio-visual receiver.

In one example, the processor 202 may be a clock-driven computingcomponent configured to process input data according to instructionsstored in the memory 206. The memory 206 may be a tangiblecomputer-readable medium configured to store instructions executable bythe processor 202. For instance, the memory 206 may be data storage thatcan be loaded with one or more of the software components 204 executableby the processor 202 to achieve certain functions. In one example, thefunctions may involve the playback device 200 retrieving audio data froman audio source or another playback device. In another example, thefunctions may involve the playback device 200 sending audio data toanother device or playback device on a network. In yet another example,the functions may involve pairing of the playback device 200 with one ormore playback devices to create a multi-channel audio environment.

Certain functions may involve the playback device 200 synchronizingplayback of audio content with one or more other playback devices.During synchronous playback, a listener will preferably not be able toperceive time-delay differences between playback of the audio content bythe playback device 200 and the one or more other playback devices. U.S.Pat. No. 8,234,395 entitled, “System and method for synchronizingoperations among a plurality of independently clocked digital dataprocessing devices,” which is hereby incorporated by reference, providesin more detail some examples for audio playback synchronization amongplayback devices.

The memory 206 may further be configured to store data associated withthe playback device 200, such as one or more zones and/or zone groupsthe playback device 200 is a part of, audio sources accessible by theplayback device 200, or a playback queue that the playback device 200(or some other playback device) may be associated with. The data may bestored as one or more state variables that are periodically updated andused to describe the state of the playback device 200. The memory 206may also include the data associated with the state of the other devicesof the media system, and shared from time to time among the devices sothat one or more of the devices have the most recent data associatedwith the system. Other embodiments are also possible.

The audio processing components 208 may include one or more ofdigital-to-analog converters (DAC), analog-to-digital converters (ADC),audio preprocessing components, audio enhancement components, and adigital signal processor (DSP), among others. In one embodiment, one ormore of the audio processing components 208 may be a subcomponent of theprocessor 202. In one example, audio content may be processed and/orintentionally altered by the audio processing components 208 to produceaudio signals. The produced audio signals may then be provided to theaudio amplifier(s) 210 for amplification and playback through speaker(s)212. Particularly, the audio amplifier(s) 210 may include devicesconfigured to amplify audio signals to a level for driving one or moreof the speakers 212. The speaker(s) 212 may include an individualtransducer (e.g., a “driver”) or a complete speaker system involving anenclosure with one or more drivers. A particular driver of thespeaker(s) 212 may include, for example, a subwoofer (e.g., for lowfrequencies), a mid-range driver (e.g., for middle frequencies), and/ora tweeter (e.g., for high frequencies). In some cases, each transducerin the one or more speakers 212 may be driven by an individualcorresponding audio amplifier of the audio amplifier(s) 210. In additionto producing analog signals for playback by the playback device 200, theaudio processing components 208 may be configured to process audiocontent to be sent to one or more other playback devices for playback.

Audio content to be processed and/or played back by the playback device200 may be received from an external source, such as via an audioline-in input connection (e.g., an auto-detecting 3.5 mm audio line-inconnection) or the network interface 214.

The microphone(s) 220 may include an audio sensor configured to convertdetected sounds into electrical signals. The electrical signal may beprocessed by the audio processing components 208 and/or the processor202. The microphone(s) 220 may be positioned in one or more orientationsat one or more locations on the playback device 200. The microphone(s)220 may be configured to detect sound within one or more frequencyranges. In one case, one or more of the microphone(s) 220 may beconfigured to detect sound within a frequency range of audio that theplayback device 200 is capable or rendering. In another case, one ormore of the microphone(s) 220 may be configured to detect sound within afrequency range audible to humans. Other examples are also possible.

The network interface 214 may be configured to facilitate a data flowbetween the playback device 200 and one or more other devices on a datanetwork. As such, the playback device 200 may be configured to receiveaudio content over the data network from one or more other playbackdevices in communication with the playback device 200, network deviceswithin a local area network, or audio content sources over a wide areanetwork such as the Internet. In one example, the audio content andother signals transmitted and received by the playback device 200 may betransmitted in the form of digital packet data containing an InternetProtocol (IP)-based source address and IP-based destination addresses.In such a case, the network interface 214 may be configured to parse thedigital packet data such that the data destined for the playback device200 is properly received and processed by the playback device 200.

As shown, the network interface 214 may include wireless interface(s)216 and wired interface(s) 218. The wireless interface(s) 216 mayprovide network interface functions for the playback device 200 towirelessly communicate with other devices (e.g., other playbackdevice(s), speaker(s), receiver(s), network device(s), control device(s)within a data network the playback device 200 is associated with) inaccordance with a communication protocol (e.g., any wireless standardincluding IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4Gmobile communication standard, and so on). The wired interface(s) 218may provide network interface functions for the playback device 200 tocommunicate over a wired connection with other devices in accordancewith a communication protocol (e.g., IEEE 802.3). While the networkinterface 214 shown in FIG. 2 includes both wireless interface(s) 216and wired interface(s) 218, the network interface 214 may in someembodiments include only wireless interface(s) or only wiredinterface(s).

In one example, the playback device 200 and one other playback devicemay be paired to play two separate audio components of audio content.For instance, playback device 200 may be configured to play a leftchannel audio component, while the other playback device may beconfigured to play a right channel audio component, thereby producing orenhancing a stereo effect of the audio content. The paired playbackdevices (also referred to as “bonded playback devices”) may further playaudio content in synchrony with other playback devices.

In another example, the playback device 200 may be sonicallyconsolidated with one or more other playback devices to form a single,consolidated playback device. A consolidated playback device may beconfigured to process and reproduce sound differently than anunconsolidated playback device or playback devices that are paired,because a consolidated playback device may have additional speakerdrivers through which audio content may be rendered. For instance, ifthe playback device 200 is a playback device designed to render lowfrequency range audio content (i.e. a subwoofer), the playback device200 may be consolidated with a playback device designed to render fullfrequency range audio content. In such a case, the full frequency rangeplayback device, when consolidated with the low frequency playbackdevice 200, may be configured to render only the mid and high frequencycomponents of audio content, while the low frequency range playbackdevice 200 renders the low frequency component of the audio content. Theconsolidated playback device may further be paired with a singleplayback device or yet another consolidated playback device.

By way of illustration, SONOS, Inc. presently offers (or has offered)for sale certain playback devices including a “PLAY:1,” “PLAY:3,”“PLAY:5,” “PLAYBAR,” “CONNECT:AMP,” “CONNECT,” and “SUB.” Any otherpast, present, and/or future playback devices may additionally oralternatively be used to implement the playback devices of exampleembodiments disclosed herein. Additionally, it is understood that aplayback device is not limited to the example illustrated in FIG. 2 orto the SONOS product offerings. For example, a playback device mayinclude a wired or wireless headphone. In another example, a playbackdevice may include or interact with a docking station for personalmobile media playback devices. In yet another example, a playback devicemay be integral to another device or component such as a television, alighting fixture, or some other device for indoor or outdoor use.

b. Example Playback Zone Configurations

Referring back to the media playback system 100 of FIG. 1, theenvironment may have one or more playback zones, each with one or moreplayback devices. The media playback system 100 may be established withone or more playback zones, after which one or more zones may be added,or removed to arrive at the example configuration shown in FIG. 1. Eachzone may be given a name according to a different room or space such asan office, bathroom, master bedroom, bedroom, kitchen, dining room,living room, and/or balcony. In one case, a single playback zone mayinclude multiple rooms or spaces. In another case, a single room orspace may include multiple playback zones.

As shown in FIG. 1, the balcony, dining room, kitchen, bathroom, office,and bedroom zones each have one playback device, while the living roomand master bedroom zones each have multiple playback devices. In theliving room zone, playback devices 104, 106, 108, and 110 may beconfigured to play audio content in synchrony as individual playbackdevices, as one or more bonded playback devices, as one or moreconsolidated playback devices, or any combination thereof. Similarly, inthe case of the master bedroom, playback devices 122 and 124 may beconfigured to play audio content in synchrony as individual playbackdevices, as a bonded playback device, or as a consolidated playbackdevice.

In one example, one or more playback zones in the environment of FIG. 1may each be playing different audio content. For instance, the user maybe grilling in the balcony zone and listening to hip hop music beingplayed by the playback device 102 while another user may be preparingfood in the kitchen zone and listening to classical music being playedby the playback device 114. In another example, a playback zone may playthe same audio content in synchrony with another playback zone. Forinstance, the user may be in the office zone where the playback device118 is playing the same rock music that is being playing by playbackdevice 102 in the balcony zone. In such a case, playback devices 102 and118 may be playing the rock music in synchrony such that the user mayseamlessly (or at least substantially seamlessly) enjoy the audiocontent that is being played out-loud while moving between differentplayback zones.

Synchronization among playback zones may be achieved in a manner similarto that of synchronization among playback devices, as described inpreviously referenced U.S. Pat. No. 8,234,395.

As suggested above, the zone configurations of the media playback system100 may be dynamically modified, and in some embodiments, the mediaplayback system 100 supports numerous configurations. For instance, if auser physically moves one or more playback devices to or from a zone,the media playback system 100 may be reconfigured to accommodate thechange(s). For instance, if the user physically moves the playbackdevice 102 from the balcony zone to the office zone, the office zone maynow include both the playback device 118 and the playback device 102.The playback device 102 may be paired or grouped with the office zoneand/or renamed if so desired via a control device such as the controldevices 126 and 128. On the other hand, if the one or more playbackdevices are moved to a particular area in the home environment that isnot already a playback zone, a new playback zone may be created for theparticular area.

Further, different playback zones of the media playback system 100 maybe dynamically combined into zone groups or split up into individualplayback zones. For instance, the dining room zone and the kitchen zone114 may be combined into a zone group for a dinner party such thatplayback devices 112 and 114 may render audio content in synchrony. Onthe other hand, the living room zone may be split into a television zoneincluding playback device 104, and a listening zone including playbackdevices 106, 108, and 110, if the user wishes to listen to music in theliving room space while another user wishes to watch television.

c. Example Control Devices

FIG. 3 shows a functional block diagram of an example control device 300that may be configured to be one or both of the control devices 126 and128 of the media playback system 100. As shown, the control device 300may include a processor 302, memory 304, a network interface 306, a userinterface 308, and microphone(s) 310. In one example, the control device300 may be a dedicated controller for the media playback system 100. Inanother example, the control device 300 may be a network device on whichmedia playback system controller application software may be installed,such as for example, an iPhone™, iPad™ or any other smart phone, tabletor network device (e.g., a networked computer such as a PC or Mac™).

The processor 302 may be configured to perform functions relevant tofacilitating user access, control, and configuration of the mediaplayback system 100. The memory 304 may be configured to storeinstructions executable by the processor 302 to perform those functions.The memory 304 may also be configured to store the media playback systemcontroller application software and other data associated with the mediaplayback system 100 and the user.

The microphone(s) 310 may include an audio sensor configured to convertdetected sounds into electrical signals. The electrical signal may beprocessed by the processor 302. In one case, if the control device 300is a device that may also be used as a means for voice communication orvoice recording, one or more of the microphone(s) 310 may be amicrophone for facilitating those functions. For instance, the one ormore of the microphone(s) 310 may be configured to detect sound within afrequency range that a human is capable of producing and/or a frequencyrange audible to humans. Other examples are also possible.

In one example, the network interface 306 may be based on an industrystandard (e.g., infrared, radio, wired standards including IEEE 802.3,wireless standards including IEEE 802.11a, 802.11b, 802.11g, 802.11n,802.11ac, 802.15, 4G mobile communication standard, and so on). Thenetwork interface 306 may provide a means for the control device 300 tocommunicate with other devices in the media playback system 100. In oneexample, data and information (e.g., such as a state variable) may becommunicated between control device 300 and other devices via thenetwork interface 306. For instance, playback zone and zone groupconfigurations in the media playback system 100 may be received by thecontrol device 300 from a playback device or another network device, ortransmitted by the control device 300 to another playback device ornetwork device via the network interface 306. In some cases, the othernetwork device may be another control device.

Playback device control commands such as volume control and audioplayback control may also be communicated from the control device 300 toa playback device via the network interface 306. As suggested above,changes to configurations of the media playback system 100 may also beperformed by a user using the control device 300. The configurationchanges may include adding/removing one or more playback devices to/froma zone, adding/removing one or more zones to/from a zone group, forminga bonded or consolidated player, separating one or more playback devicesfrom a bonded or consolidated player, among others. Accordingly, thecontrol device 300 may sometimes be referred to as a controller, whetherthe control device 300 is a dedicated controller or a network device onwhich media playback system controller application software isinstalled.

The user interface 308 of the control device 300 may be configured tofacilitate user access and control of the media playback system 100, byproviding a controller interface such as the controller interface 400shown in FIG. 4. The controller interface 400 includes a playbackcontrol region 410, a playback zone region 420, a playback status region430, a playback queue region 440, and an audio content sources region450. The user interface 400 as shown is just one example of a userinterface that may be provided on a network device such as the controldevice 300 of FIG. 3 (and/or the control devices 126 and 128 of FIG. 1)and accessed by users to control a media playback system such as themedia playback system 100. Other user interfaces of varying formats,styles, and interactive sequences may alternatively be implemented onone or more network devices to provide comparable control access to amedia playback system.

The playback control region 410 may include selectable (e.g., by way oftouch or by using a cursor) icons to cause playback devices in aselected playback zone or zone group to play or pause, fast forward,rewind, skip to next, skip to previous, enter/exit shuffle mode,enter/exit repeat mode, enter/exit cross fade mode. The playback controlregion 410 may also include selectable icons to modify equalizationsettings, and playback volume, among other possibilities.

The playback zone region 420 may include representations of playbackzones within the media playback system 100. In some embodiments, thegraphical representations of playback zones may be selectable to bringup additional selectable icons to manage or configure the playback zonesin the media playback system, such as a creation of bonded zones,creation of zone groups, separation of zone groups, and renaming of zonegroups, among other possibilities.

For example, as shown, a “group” icon may be provided within each of thegraphical representations of playback zones. The “group” icon providedwithin a graphical representation of a particular zone may be selectableto bring up options to select one or more other zones in the mediaplayback system to be grouped with the particular zone. Once grouped,playback devices in the zones that have been grouped with the particularzone will be configured to play audio content in synchrony with theplayback device(s) in the particular zone. Analogously, a “group” iconmay be provided within a graphical representation of a zone group. Inthis case, the “group” icon may be selectable to bring up options todeselect one or more zones in the zone group to be removed from the zonegroup. Other interactions and implementations for grouping andungrouping zones via a user interface such as the user interface 400 arealso possible. The representations of playback zones in the playbackzone region 420 may be dynamically updated as playback zone or zonegroup configurations are modified.

The playback status region 430 may include graphical representations ofaudio content that is presently being played, previously played, orscheduled to play next in the selected playback zone or zone group. Theselected playback zone or zone group may be visually distinguished onthe user interface, such as within the playback zone region 420 and/orthe playback status region 430. The graphical representations mayinclude track title, artist name, album name, album year, track length,and other relevant information that may be useful for the user to knowwhen controlling the media playback system via the user interface 400.

The playback queue region 440 may include graphical representations ofaudio content in a playback queue associated with the selected playbackzone or zone group. In some embodiments, each playback zone or zonegroup may be associated with a playback queue containing informationcorresponding to zero or more audio items for playback by the playbackzone or zone group. For instance, each audio item in the playback queuemay comprise a uniform resource identifier (URI), a uniform resourcelocator (URL) or some other identifier that may be used by a playbackdevice in the playback zone or zone group to find and/or retrieve theaudio item from a local audio content source or a networked audiocontent source, possibly for playback by the playback device.

In one example, a playlist may be added to a playback queue, in whichcase information corresponding to each audio item in the playlist may beadded to the playback queue. In another example, audio items in aplayback queue may be saved as a playlist. In a further example, aplayback queue may be empty, or populated but “not in use” when theplayback zone or zone group is playing continuously streaming audiocontent, such as Internet radio that may continue to play untilotherwise stopped, rather than discrete audio items that have playbackdurations. In an alternative embodiment, a playback queue can includeInternet radio and/or other streaming audio content items and be “inuse” when the playback zone or zone group is playing those items. Otherexamples are also possible.

When playback zones or zone groups are “grouped” or “ungrouped,”playback queues associated with the affected playback zones or zonegroups may be cleared or re-associated. For example, if a first playbackzone including a first playback queue is grouped with a second playbackzone including a second playback queue, the established zone group mayhave an associated playback queue that is initially empty, that containsaudio items from the first playback queue (such as if the secondplayback zone was added to the first playback zone), that contains audioitems from the second playback queue (such as if the first playback zonewas added to the second playback zone), or a combination of audio itemsfrom both the first and second playback queues. Subsequently, if theestablished zone group is ungrouped, the resulting first playback zonemay be re-associated with the previous first playback queue, or beassociated with a new playback queue that is empty or contains audioitems from the playback queue associated with the established zone groupbefore the established zone group was ungrouped. Similarly, theresulting second playback zone may be re-associated with the previoussecond playback queue, or be associated with a new playback queue thatis empty, or contains audio items from the playback queue associatedwith the established zone group before the established zone group wasungrouped. Other examples are also possible.

Referring back to the user interface 400 of FIG. 4, the graphicalrepresentations of audio content in the playback queue region 440 mayinclude track titles, artist names, track lengths, and other relevantinformation associated with the audio content in the playback queue. Inone example, graphical representations of audio content may beselectable to bring up additional selectable icons to manage and/ormanipulate the playback queue and/or audio content represented in theplayback queue. For instance, a represented audio content may be removedfrom the playback queue, moved to a different position within theplayback queue, or selected to be played immediately, or after anycurrently playing audio content, among other possibilities. A playbackqueue associated with a playback zone or zone group may be stored in amemory on one or more playback devices in the playback zone or zonegroup, on a playback device that is not in the playback zone or zonegroup, and/or some other designated device.

The audio content sources region 450 may include graphicalrepresentations of selectable audio content sources from which audiocontent may be retrieved and played by the selected playback zone orzone group. Discussions pertaining to audio content sources may be foundin the following section.

d. Example Audio Content Sources

As indicated previously, one or more playback devices in a zone or zonegroup may be configured to retrieve for playback audio content (e.g.according to a corresponding URI or URL for the audio content) from avariety of available audio content sources. In one example, audiocontent may be retrieved by a playback device directly from acorresponding audio content source (e.g., a line-in connection). Inanother example, audio content may be provided to a playback device overa network via one or more other playback devices or network devices.

Example audio content sources may include a memory of one or moreplayback devices in a media playback system such as the media playbacksystem 100 of FIG. 1, local music libraries on one or more networkdevices (such as a control device, a network-enabled personal computer,or a networked-attached storage (NAS), for example), streaming audioservices providing audio content via the Internet (e.g., the cloud), oraudio sources connected to the media playback system via a line-in inputconnection on a playback device or network devise, among otherpossibilities.

In some embodiments, audio content sources may be regularly added orremoved from a media playback system such as the media playback system100 of FIG. 1. In one example, an indexing of audio items may beperformed whenever one or more audio content sources are added, removedor updated. Indexing of audio items may involve scanning foridentifiable audio items in all folders/directory shared over a networkaccessible by playback devices in the media playback system, andgenerating or updating an audio content database containing metadata(e.g., title, artist, album, track length, among others) and otherassociated information, such as a URI or URL for each identifiable audioitem found. Other examples for managing and maintaining audio contentsources may also be possible.

The above discussions relating to playback devices, control devices,playback zone configurations, and media item sources provide only someexamples of operating environments within which functions and methodsdescribed below may be implemented. Other operating environments andconfigurations of media playback systems, playback devices, and networkdevices not explicitly described herein may also be applicable andsuitable for implementation of the functions and methods.

e. Example Cloud Computing Functions

Various references are made herein to “cloud computing.” The term “cloudcomputing” is used to refer to services delivered using distributedcomputing over a network, such as the Internet. A non-exhaustive list ofservices delivered via the cloud include electronic mail (e.g., GMAIL®or HOTMAIL®), social networking (e.g., FACEBOOK®, LINKEDIN®, orTWITTER®), file hosting (e.g., DROPBOX®), and streaming audio (e.g.,SPOTIFY®, PANDORA®, or BEATSAUDIO®). Other cloud services are certainlyoffered as well.

Cloud service providers may offer one or more interfaces for accessingtheir service over a network. For instance, some cloud services may beaccessed by visiting a web site using a web browser. Other cloudservices are accessed by executing a particular application specific tothe cloud service on a computing device. Some cloud services may offeran application programming interface (API) to facilitate access to theservice by a third-party web site or application. Cloud services mayprovide multiple techniques for accessing their service. In many cases,a user who has access to a given cloud service can access the servicefrom any computing device that is connected to the network, providedthat the computing device has a supported interface to the cloudservice.

In one instance, accessing a cloud service may involve accessing, with afirst computing device (i.e., a client), a second computing device orsystem (i.e., a server). Example client devices may include playbackdevice 200 of FIG. 2, or control device 300 of FIG. 3, among otherpossible devices. One or more programs or applications (i.e.,instructions) may execute on the server to perform computing operationssupported by the cloud service. The client may send various commands tothe server to instruct the server to perform the computing taskssupported by the cloud service.

FIG. 5 illustrates an example computing system 500 that may provide acloud service to one or more users. Example computing system 500includes at least one processor 502, memory 504, and a network interface506. The memory 504 may contain instructions executable by the processor502 to perform computing tasks supported by a cloud service. Thecomputing device 500 may communicate with other computing devices viathe network interface 506.

In aggregate, the provision of a cloud service many involve multipleinstances of computing system 500. Each instance of computing system 500may provide the cloud service to one or more users. Cloud serviceproviders may scale the number of instances of computing system 500involved in providing the cloud service based on user demand.

In some embodiments contemplated herein, a cloud service provider mayprovide a neural network that has been trained to detect a set of knownimpairments or conditions. Such a cloud service provider may include oneor more instances of a computing system, such as computing system 500,each of which may host an instance of the neural network (or a portionthereof). Client devices (e.g., playback devices and/or control devices)of a media playback system may provide input (e.g., input vector(s)) tothe neural network, and receive an indication of which neurons of theneural network “fired” and thereby indicate the presence or absence ofthe known impairments or conditions.

III. Example Playback Device Calibration

As previously discussed, one or more playback devices, such as one ormore of the playback devices 102-124 of FIG. 1, may be configured toprovide a particular audio experience, and may be calibrated to providethat audio experience regardless of position(s) of the one or moreplayback devices within the playback environment. As noted above,example calibration procedures contemplated herein may involve amicrophone of a recording device detecting and analyzing sound waves(e.g., one or more calibration sounds) emitted by the playback device(s)under calibration.

A calibration interface may be provided on a network device to guide auser through the calibration process. Example interfaces are describedin U.S. non-provisional patent application Ser. No. 14/696,014, filed onApr. 24, 2015, entitled “Speaker Calibration,” which is incorporatedherein in its entirety. Further example interfaces are described in U.S.non-provisional patent application Ser. No. 14/826,873, filed on Aug.14, 2015, entitled “Speaker Calibration User Interface,” which is alsoincorporated herein in its entirety. Alternatively, calibration may beperformed automatically between the network device and the playbackdevice(s), and may be conducted with or without interaction by a user ofthe network device. The network device may be a device the user can useto control the one or more playback devices. For instance, the networkdevice may be similar to the control devices 126 and 128 of FIGS. 1, and300 of FIG. 3. The calibration interface may be a component of acontroller interface, such as the controller interface 400 of FIG. 4that is provided on the network device for controlling the one or moreplayback devices.

Once the one or more playback devices have been positioned within theplayback environment, the calibration interface may cause the one ormore playback devices to play a calibration tone. Particular calibrationtones may facilitate example calibration procedures contemplated here.Example calibration tones are described in U.S. non-provisional patentapplication Ser. No. 14/805,140, filed on Jul. 21, 2015, entitled“Hybrid Test Tone for Space-Averaged Room Audio Calibration Using AMoving Microphone,” which is incorporated herein in its entirety.

The network device may be positioned so as to receive the audio datarelated to the playback of the calibration tone by the one or moreplayback devices. In one example, the interface may prompt the user tomove the network device within the playback environment while thecalibration tone is playing. For example, in one more specific case, theinterface may instruct the user to traverse areas within the playbackenvironment where enjoyment of audio playback by the one or moreplayback devices may typically occurs. In another example, the interfacemay instruct the user to move the network device as close as possible toopposing border regions of the playback environment, such as walls in aroom. In one case, the calibration interface may provide a videodemonstrating how a user may traverse a playback environment. The videomay be shown to the user via the interface before the calibration toneis played or while the calibration tone is playing. Examples of a movingmicrophone during calibration are described in U.S. non-provisionalpatent application Ser. No. 14/481,511, filed on Sep. 9, 2014, entitled“Playback Device Calibration,” which is incorporated herein in itsentirety.

In some examples, multiple playback devices may be calibratedconcurrently. Further, some playback devices may include multipleplayback channels (e.g., a tweeter and a woofer, or multiple speakersconfigured to act as a channel), which may be calibrated concurrently.Example techniques to facilitate calibration of multiple playbackchannels are described in U.S. non-provisional patent application Ser.No. 14/805,340, filed on Jul. 21, 2015, entitled “ConcurrentMulti-Loudspeaker Calibration with a Single Measurement,” which isincorporated herein in its entirety.

In one example, the calibration tone may be played for a predeterminedduration of time, and the user may be allocated the predeterminedduration of time to traverse the areas in the playback environment. Inanother example, the amount of time that the calibration tone is playedback may be modified based on information sensed by the network deviceregarding the motion or path of the network device. For instance, if thenetwork device determines that that the network device has started tobacktrack across a previously traversed path, the network device maydetermine that no additional measurement of the calibration tone isnecessary and may cause playback of the calibration tone by the one ormore playback devices to be terminated.

In a further example, the amount of time that the calibration tone isplayed back may be modified based on the detected audio signal. Forinstance, if the network device determines that additional samples ofthe audio signal detected in the playback environment will not improvethe determination of the characterization of the playback environment,the network device may determine that no additional measurement ofcalibration tone is necessary and may cause playback of the calibrationtone by the one or more playback devices to be terminated. Otherexamples are also possible.

The predetermined duration of time may vary depending on a type and/orsize of the playback environment. For instance, prior to causing the oneor more playback device to play the calibration tone, the calibrationinterface may prompt the user to indicate a type and/or a size of theplayback environment. Based on the user's input, the interface mayidentify an appropriate predetermined duration of time to play thecalibration tone based on the indicated type and/or size of the playbackenvironment. In one case, the provided demonstration video may also varybased on the indicated type and/or size of the playback environment. Inanother example, the user may be instructed to move between opposingborder areas of the playback environment. The approximate size of theplayback environment may be determined based on a detected motion and/orpath of the network device, so that the playback time of the calibrationtone may be adjusted (extended or shortened) based on the detectedmotion and/or detected path of motion of the user. For example, if it isdetected that the user is still moving the network device, thecalibration tone playback may be extended. In another example, if it isdetected that the user is moving the device in a direction thatindicates that the playback environment is larger than previouslyassumed and that the user needs more time to properly move the device tocover the entire or a substantial portion of the playback environment,the playback time may be extended.

While the one or more playback devices is playing the calibration tone,a microphone of the network device, such as microphone 310 of thecontrol device 300, may detect an audio signal. A processor of thenetwork device, such as the processor 302 of the control device 300, mayreceive a stream of audio data from the microphone as the audio signalis detected. The processor may then process the received audio data todetermine audio characteristics of the playback environment. Forinstance, a linear frequency response associated with the playbackenvironment may be determined based on the audio data.

A signal processing algorithm may then be determined based on the audiocharacteristics. For instance, equalization parameters may be determinedsuch that when the equalization parameters are applied by the one ormore playback device when playing audio content, a particular audioexperience may be created. In other words, a calibration profile may beapplied to a playback device to offset acoustic characteristics of theenvironment.

IV. Example Techniques To Train A Neural Network To Detect Impairments

Some example embodiments described here may facilitate training a neuralnetwork to detect specific impairments or other conditions when providedinput data (e.g., sensor data) characteristic of one or more of theimpairments.

FIG. 6 shows a flow diagram of example implementation 600 for training aneural network. Implementation 600 presents example techniques that canbe implemented within an operating environment involving, for example,the media playback system 100 of FIG. 1, one or more of the playbackdevice 200 of FIG. 2, and/or one or more of the control device 300. Inone example, the implementation 600 may be performed in whole or in partby a computing system in communication with a media playback system. Forinstance, the implementation 500 may be performed by one or more ofcomputing system 500 of FIG. 5. In such cases, one or more of thecomputing system 500 may have installed thereon a software applicationthat includes instructions executable by one or more processors of arespective computing system to cause the respective device(s) or systemto perform the functions of implementation 600.

Implementation 600 may include one or more operations, functions, oractions as illustrated by one or more of blocks 602-612. Although therespective blocks are illustrated in sequential order, these blocks mayalso be performed in parallel, and/or in a different order than thosedescribed herein. Also, the various blocks may be combined into fewerblocks, divided into additional blocks, and/or removed based upon thedesired implementation. In addition, for the implementation 500 andother processes and methods disclosed herein, the flowcharts showfunctionality and operation of only a few possible implementations ofpresent embodiments. In this regard, each block may represent a module,a segment, or a portion of program code, which includes one or moreinstructions executable by a processor for implementing specific logicalfunctions or steps in the process. The program code may be stored on anytype of computer readable medium, for example, such as a storage deviceincluding a disk or hard drive.

The computer readable medium may include non-transitory computerreadable medium, for example, such as computer-readable media thatstores data for short periods of time like register memory, processorcache and Random Access Memory (RAM). The computer readable medium mayalso include non-transitory media, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. The computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device. Inaddition, for the implementation 500 and other processes and methodsdisclosed herein, each block may represent circuitry that is wired toperform the specific logical functions in the process.

a. Receive Response Matrix

At block 602, implementation 600 involves receiving a response matrix.For instance, a computing system, such as computing system 500, mayreceive a plurality of responses, which may be combined into a responsematrix. Generating such responses may involve a playback devicerecording audio emitted by a playback device with and without a set ofknown impairments applied. Each response may be a respective vector thatincludes components which represent the response of the system inrespective frequency ranges. In some cases, the responses of such asystem may be represented as power spectral densities. For instance, anexample response vector may include up to 8000 or more components, eachcomponent representing power density of the recorded audio within arespective range of frequencies.

The vectors representing the respective responses may be combined intothe response matrix. Such a response matrix may include a dimension foreach response. For example, 150 vectors representing respective powerspectral densities may be generated and combined into a response matrixP of dimension n×m (e.g., 150×8000). A portion of these responses mayrepresent responses of a playback device with respective impairmentspresent, while the remainder of responses may represent responses of aplayback device without the respective impairments present.

As noted above, example known impairments that an example neural networkmight be trained to detect may include, but are not limited to anobject(s) or obstruction(s) at least partially covering or otherwiseobstructing or affecting sound detected by one or more microphones of arecording device that is being used to record audio in connection with acalibration procedure. Such objects and/or obstructions may include, forexample, (i) a user's finger at least partially covering (typicallyinadvertently) one or more microphones that are being to detect,monitor, and/or record sound as part of the calibration procedure, (ii)a smartphone case at least partially covering or enclosing one or moresmartphone microphones that are being used to detect, monitor, and/orrecord sound as part of the calibration procedure, (iii) lint, dust, orother debris on or in one or more microphones being used to detect,monitor, and/or record sound in connection with the calibrationprocedure, (iv) objects that are on a table, shelf, or wall near thedevice comprising one or more of the microphones that are being used todetect, monitor, and/or record sound in connection with the calibrationprocedure, and/or (v) other object(s) and/or (vi) other object(s) and/orobstruction(s) that tend to affect sound waves received by the one ormore microphones being used to detect, monitor, and/or record sound inconnection with the calibration procedure.

To generate responses representing such impairments, audio can berecorded (i) using different playback devices, (ii) in different rooms,(iii) with different variants of each impairment (e.g. with differenttype of cases on a smartphone, different amounts/types of debris on themicrophone), (iv) with playback devices placed in different locationswithin a room, (v) with playback devices arranged in differentorientations (up, down, side, etc.), (vi) with multiple playback devicesarranged in different positions relative to one another within a room,(vii) using different audio test signals, and/or (viii) usingtime-varying conditions, such as audio test signals that change overtime, a user moving his/her finger in front of and then away from themicrophone, or other time-varying conditions. In some cases, to reducevariance across the set of responses, certain conditions may be heldconstant across the set of responses. For instance, the same type ofrecording device (with the same type of microphone), the same type ofplayback device, and/or the same audio test tone might be used for allof the responses.

As mentioned earlier, each individual response is represented as avector in the matrix. Some embodiments may include multiple vectors fora particular impairment, where each vector of the multiple vectors forthe impairment corresponds to a different variant of the impairment. Insome embodiments, the matrix may also include vectors for differentvariants without impairments.

In some embodiments, the responses (e.g., the power spectral density(PSD) measurements) defined by the vectors in the matrix may correspondto a full range of the PSD measurement. For example, the response totest audio from approximately 20 Hz to approximately 22 KHz, which maycorrespond to a range of frequencies that playback devices undercalibration are capable of emitting or a range that the playback devicesare to be calibrated over. In other embodiments, PSD measurementsdefined by the vectors in the matrix may be windowed or trimmed so as torepresent a specific portion of the frequency or amplitude range of aparticular response. Some embodiments may include performing thiswindowing or trimming of the PSD measurement. In some embodiments, thePSD measurement may be windowed or trimmed to include the responsebetween about 2.5 KHz to 16 KHz, which may represent frequencies overwhich the known impairments cause most variance. However, other rangesor portions of the full PSD measurement could be used as well.

Some embodiments may further include converting individual PSDmeasurements from linear spacing to log-spacing in the frequency domain,which may improve computational efficiency by reducing the number offrequency bins (i.e., components) in each vector of the response matrix.However, such conversion might not be required in all embodiments.

b. Determine Principle Components Representing Axes of Greatest VarianceIn The Response Matrix

At block 604, the implementation 600 involves determining principlecomponents from the response matrix. For instance, after recording theaudio with and without each impairment (and with the different variantsof each impairment and non-impairment case, for embodiments that includedifferent variants of each impairment and non-impairment condition) andbuilding the matrix of vectors corresponding to the different variantsof impairment and non-impairment conditions, a computing system mayextract principal components from the matrix.

The principal components are eigenvectors representing the axes ofgreatest variance in the matrix data using the fewest possible vectors.In two-dimensional space, the principal components can be characterizedas a linear direction and magnitude that explains some large percentageof a two-dimensional dataset. As noted above, an example response matrixmay include a dimension for each response (vector). In some embodiments,the matrix is a 125 to 175 dimension matrix (e.g., 150 dimensions). Byextracting principle components from this matrix, the dimensionality ofthe data set can be reduced.

Extracting the principle components from the response matrix involvesidentifying a new set of orthogonal coordinate axes through the responsematrix. This involves finding the direction of maximal variance throughthe dataset in the multi-dimensional space (e.g., the 150-dimensionresponse matrix). It is equivalent to obtaining the (least-squares) lineof best fit through plotted data representing the response matrix. Theaxis of maximal variance is the first principal component of the data.After obtaining the direction of maximal variance through the responsematrix dataset, additional principle components of the data aredetermined. Each subsequent principle component (i.e., the second,third, fourth, and fifth principle components) is orthogonal to theprevious principle component (e.g., the second principle component isorthogonal to the first principle component).

To illustrate, consider the example response matrix noted above, whichis an m×n matrix, X where the n columns are the components of theresponse vectors and the m rows are the responses (e.g., a 150 responseby 8000 sample response matrix, X). To extract principle components,this matrix X is linearly transformed into another matrix Y, so that forsome m×m matrix, P, Y=PX. In other words, X is being projected onto thecolumns of P. Thus, the rows of P are a new basis for representing thecolumns of X (i.e., the principle component directions).

To linearly transform the original response matrix X using the relationY=PX for some matrix P, the processing device determines a diagonalcovariance matrix C_(y) for Y.

$C_{Y} = {{\frac{1}{n - 1}{YY}^{T}} = {{\frac{1}{n - 1}({PX})({PX})^{T}} = {{\frac{1}{n - 1}({PX})\left( {X^{T}P^{T}} \right)} = {\frac{1}{n - 1}{P\left( {XX}^{T} \right)}P^{T}}}}}$$C_{Y} = {{\frac{1}{n - 1}{PSP}^{T}\mspace{14mu}{where}\mspace{14mu} S} = {XX}^{T}}$

S is an m×m symmetric matrix, since (XX^(T))^(T)=(XT)^(T)(X)^(T)=XX^(T).Since every square symmetric matrix is orthogonally diagonalizable,S=EDE^(T), where E is an m×m orthonormal matrix which the orthonormaleigenvectors of S as its columns and D is a diagonal matrix that has theeigenvalues of S as its diagonal entries. In the transformation matrix,P, the rows of P are the eigenvectors of S. Substituting this into thediagonal covariance matrix C_(y) for Y yields

$C_{Y} = {{\frac{1}{n - 1}{PSP}^{T}} = {\frac{1}{n - 1}E^{T}{EDE}^{T}{E.}}}$Since E is an orthonormal matrix, E^(T)E=I, where I is the m×m identitymatrix. Then for P where the rows of P are the eigenvectors of S,

$C_{Y} = {\frac{1}{n - 1}{D.}}$c. Determine a Principle-Component Matrix That Represents A Given Set OfThe Principle Components

At block 606, the implementation 600 involves determining aprinciple-component matrix that represents a given set of the principlecomponents. For instance, after extracting the principal components fromthe matrix, the principal components may be considered in order ofdecreasing error, and some number of the most accurate principlecomponents (i.e., the principal components having the least error) areselected for inclusion in a smaller matrix.

For example, in an embodiment with a few hundred vectors (i.e., onevector for each response, where each response is a PSD measurement of aparticular variant as described above) in the matrix, some embodimentsmay select the top 6-12 (or some other number) most accurate principalcomponents (i.e. the principle components that represent the axes ofgreatest variance). In one example having a 150 dimensional matrix, thetop eight most accurate principal components may be selected and placedinto an 8×150 matrix (i.e., 8 principal components×150 responses), whichrepresents a new simplified space that characterizes a multidimensionalvariance of the teaching dataset.

To obtain the relative amount of variance represented by each principle,referring back to the above example, the processing device may determinethe eigenvalues and eigenvectors of S=XX^(T). These eigenvalues can besorted in descending order and placed on the diagonal of D. Theorthonormal matrix E is constructed by placing the associatedeigenvectors in the same order to form the columns of E (i.e. place theeigenvector that corresponds to the largest eigenvalue in the firstcolumn, the eigenvector corresponding to the second largest eigenvaluein the second column and so on). In this way, the principal components(the rows of P) are the eigenvectors of the covariance matrix XX^(T) andeach row is ordered based on how much of the variance of X isrepresented by that principal component.

A subset that represents the axes of greatest variance may be selectedfrom the principle components. As noted above, in one example having a150 dimension matrix, the first eight principal components may beselected and placed into an 8×150 matrix (i.e., 8 principalcomponents×150 responses), which represents a new simplified space thatcharacterizes a multidimensional variance of the teaching dataset.

In some embodiments, the computing system may store the magnitude ofeach principal component vector that represents the variance in a singledimension across all the frequency bins, and, at least in someembodiments, use the magnitudes of the principal component vectors forsubsequent determinations.

d. Determine Teaching Matrix

At block 608, the implementation 600 involves determining a teachingmatrix. For instance, the computing system may determine a teachingmatrix that represents the response matrix in a simplified space (i.e.,a matrix with fewer dimensions). To illustrate, continuing with the8×150 matrix example above, the matrix of PSDs (150×8000) may beprojected onto the primary component matrix (via matrix multiplicationto generate a representation of each PSD (i.e., each impairment andnon-impairment condition)) in a simplified PC space. The PC space is ateaching matrix representing the matrix of PSDs using the primarycomponents, which reduces the dimensionality of the response matrix.

e Train Neural Network

At block 610, the implementation 600 involves training a neural network.For instance, a computing system may train a neural network to detectthe set of known impairments. An example neural network may include aplurality of neurons. Two or more of the neurons may correspond torespective known impairments. Once trained, an input that ischaracteristic of a particular impairment will cause the neuron thatcorresponds to that impairment to “fire,” thereby indicating thepresence of the impairment in the system from which the input wascaptured.

FIG. 7 illustrates an example neuron 700 of a neural network. Neuron 700includes a series of functional operations. Upon being provided a scalarinput p, at 702 neuron 700 multiplies p by a scalar weight w to form theproduct wp. At 704, the weighted input wp is added to the scalar bias bto form the net input n. At 706, the net input n is passed through thetransfer function ƒ, which produces the scalar output α. In operation, agiven input p that produces a certain scalar output a (e.g., an outputthat exceeds a threshold) can be considered to have caused the neuron700 to “fire.”

FIG. 8 illustrates another example neuron 800 of a neural network. Incontrast to neuron 700, neuron 800 is configured to handle inputs thatare vectors (e.g., input vectors representative of responses, or PSDs ofa media playback device or system). In FIG. 8, the input to neuron 800is a vector p of size R. Upon being provided a vector input p, at 802neuron 800 multiplies p by a matrix weight W to form the product Wp. At804, the weighted input Wp is added to the scalar bias b to form the netinput n. At 806, the net input n is passed through the transfer functionƒ, which produces the scalar output α. Like with neuron 700, a giveninput p that produces a certain scalar output α (e.g., an output thatexceeds a threshold) can be considered to have caused the neuron 800 to“fire.”

As noted above, example neural networks include a plurality of neurons(e.g., a plurality of neuron 800). In some embodiments, a neural networkincludes a first neuron that corresponds to a lack of impairment, whichmay be considered a condition to detect in and of itself. When thatneuron fires, the neural network indicates that no impairment is presentin the input. The example neural network may also include a plurality ofsecond neurons that correspond to respective impairments. When aparticular second neuron fires, the neural network indicates that theimpairment or condition corresponding to that particular neuron ispresent.

The neural network may include two or more layers, each including aweight matrix W, bias vector b and output vector a. FIG. 9 depicts anexample two-layer neural network 900 that includes a hidden layer 902and an output layer 904. Each of the layers may include a plurality ofneurons (e.g., a neuron corresponding to each impairment or condition todetect). The layers of a neural network afford different functions. Theoutput layer 904 produces of the output of the neural network. All otherlayers are referred to as hidden layers (as their output is internal tothe neural network). In some examples, the hidden layer 902 is used fortraining the neural network.

Training the neural network involves tuning the weight W, bias vector band transfer function ƒ to cause a neuron to fire when input that ischaracteristic of a given impairment is provided as input to thatneuron. In some examples, the teaching matrix is provided in a loop asinput to the neural network. The output of each loop iteration iscompared to a target matrix that represents an ideal determination ofeach known impairment or condition. This output and the error (e.g.,the-mean square error) of the output as compared with the target matrixis used to iteratively train the network using backpropagation (e.g.,Levenberg-Marquardt backprogation). Under backpropagation, the output ofeach loop iteration and the error of the output as compared with thetarget matrix is used to adjust the weight W, bias vector b and transferfunction ƒ to iteratively tune the neural network to produce the desiredoutput (i.e., detection of an impairment), given an input that ischaracteristic of that impairment. The loop is repeated until a timelimit is reached, a threshold number of iterations are performed, or aconvergence threshold is met.

In some cases, characteristic responses of devices used in generatingthe response matrix may affect the training of the matrix. As notedabove, in generating a response matrix, a sound (e.g., a test tone) isemitted by playback device and recorded by a recording device, both withand without a set of impairments present. However, the particularplayback device and recording device used to generate the responses mayhave characteristic responses. For instance, a particular playbackdevice (or a certain type of playback device (e.g., a particular model))might not have a perfectly flat response and might boost somefrequencies while cutting others. As another example, a microphone of arecording device (e.g., an IPHONE® 6) might detect certain frequenciesas being relatively more or less energy dense than they actually are.These characteristic responses may affect training of the neuralnetwork, as the response matrix may be skewed by these characteristicresponses.

To offset such characteristic responses, the computing system maycompensate for the characteristic responses by training the neuralnetwork with a response matrix that has the characteristic responses ofthe devices removed. To remove the characteristic responses from theresponse measurements, the computing system may multiply (e.g., take thedot product) of each response by the inverse of the characteristicresponse of each device used, thereby yielding a response dataset thatis not influenced by the characteristic responses of the devices used togenerate the response matrix.

f. Store Neural Network

At block 612, the implementation 600 involves storing the neuralnetwork. For instance, the trained neural network is stored on one moreinstances of computing system 500, which are providing a cloud serviceto media playback systems (e.g., media playback system 100). Then, whena given media playback system performs a calibration on one or moreplayback devices, that media playback can provide response(s) of the oneor more playback devices to a given instance of computing system 500.Computing system 500 can provide the response(s) to the neural networkto detect any impairments that might be affecting the given mediaplayback system and thereafter notify that media playback system of anysuch impairments. Further, the response(s) from the given media playbacksystem may be used to further tune the neural network, using thebackpropagation techniques described above.

Alternatively, one or more devices of a media playback system may storethe trained neural network for use in detecting impairments or otherconditions. Such devices might include one or more playback devices(e.g., one or more of playback device 200) and/or one or more controldevices (e.g., one or more of control device 300). From time to time, anupdated version of the neural network may be distributed to the mediaplayback systems, perhaps from one more instances of computing system500 via a network (e.g., from a cloud service).

V. Example Techniques to Detect Impairments Using A Neural Network

As noted above, example embodiments described herein may involve using aneural network to detect impairments.

FIG. 10 shows a flow diagram of example implementation 1000 to detectimpairments or other conditions. Implementation 1000 presents exampletechniques that can be implemented within an operating environmentinvolving, for example, the media playback system 100 of FIG. 1, one ormore of the playback device 200 of FIG. 2, and one or more of thecontrol device 300. In one example, the implementation 1000 may beperformed in whole or in part by a computing device in communicationwith a media playback system. For instance, the implementation 800 maybe performed by one or more of the control devices 126 and 128 ofFIG. 1. In such cases, one or more of the control devices 126 and 128may have installed thereon a software application that includesinstructions executable by a processor of a respective control device tocause the respective control device to perform the functions ofimplementation 800.

Implementation 1000 may include one or more operations, functions, oractions as illustrated by one or more of blocks 1002-1010. Although therespective blocks are illustrated in sequential order, these blocks mayalso be performed in parallel, and/or in a different order than thosedescribed herein. Also, the various blocks may be combined into fewerblocks, divided into additional blocks, and/or removed based upon thedesired implementation. In addition, for the implementation 1000 andother processes and methods disclosed herein, the flowcharts showfunctionality and operation of only a few possible implementations ofpresent embodiments. In this regard, each block may represent a module,a segment, or a portion of program code, which includes one or moreinstructions executable by a processor for implementing specific logicalfunctions or steps in the process. The program code may be stored on anytype of computer readable medium, for example, such as a storage deviceincluding a disk or hard drive.

The computer readable medium may include non-transitory computerreadable medium, for example, such as computer-readable media thatstores data for short periods of time like register memory, processorcache and Random Access Memory (RAM). The computer readable medium mayalso include non-transitory media, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. The computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device. Inaddition, for the implementation 1000 and other processes and methodsdisclosed herein, each block may represent circuitry that is wired toperform the specific logical functions in the process.

a. Receive Data Indicating A Response Of One Or More Playback Devices

At block 1002, implementation 1000 involves a media playback systemreceiving data indicating a response of one or more playback devices.For instance, referring to media playback system 100 of FIG. 1, controldevice 128 may detect, via a microphone, audio emitted by playbackdevice 120, yielding a measurement indicative of a response of playbackdevice 120. Based on such recorded audio, control device 128 maydetermine a response (e.g., a power spectral density or frequencyresponse) of playback device 120 as captured by control device 128 inthe bedroom listening area. This response may be represented as a vector(i.e., a response vector) containing a number of components, eachrepresenting response at a respective frequency (or frequency range).

In some embodiments, a recording device (e.g., control device 126 or128) may capture audio from one or more playback devices during acalibration procedure. As noted above, some calibration procedurescontemplated herein involve a recording device (e.g., a control device)of the media playback system detecting and analyzing sound waves (e.g.,one or more calibration sounds) which were emitted by one or moreplayback devices of the media playback system. From such detected soundwaves, the recording device (or another device of the media playbacksystem) may determine a response of the one or more playback devices.

In some embodiments, the control device may analyze the calibrationsound over a range of frequencies over which the playback device is tobe calibrated (i.e., a calibration range). Accordingly, the particularcalibration sound that is emitted by a playback device may cover thecalibration frequency range by including sound at frequencies withinthat range. The calibration frequency range may include a range offrequencies that the playback device is capable of emitting (e.g.,15-30,000 Hz) and may be inclusive of frequencies that are considered tobe in the range of human hearing (e.g., 20-22,000 Hz). By emitting andsubsequently detecting a calibration sound covering such a range offrequencies, a response that is inclusive of that range may bedetermined for the playback device. Such a frequency response may berepresentative of the environment in which the playback device emittedthe calibration sound (and perhaps also representative of a knownimpairment or other condition that is present).

In some embodiments, the media playback system may determine a response(e.g., a PSD measurement) that corresponds to a full range of the PSDmeasurement. For instance, such a response may correspond to a range offrequencies that playback devices under calibration are capable ofemitting (e.g., from 15-30,000 Hz) or possibly a range that the playbackdevices are to be calibrated over (e.g., 20-22,000 Hz). In otherembodiments, the response measurement may be windowed or trimmed so asto represent a specific portion of the frequency or amplitude range of aparticular response. Some embodiments may include performing thiswindowing or trimming of the response measurement. Within examples, theresponse measurement may be windowed or trimmed to include the responsebetween about 2.5 KHz to 16 KHz, which may represent frequencies overwhich the known impairments are understood to cause most variance.However, other ranges or portions of the full response measurement couldbe used as well.

Some example calibration procedures may involve detecting thecalibration sound at multiple physical locations within the environment,which may assist in capturing acoustic variability within theenvironment. To facilitate detecting the calibration sound at multiplepoints within an environment, some calibration procedures involve amoving microphone. For example, the microphone that is detecting thecalibration sound may be continuously moved through the environmentwhile the calibration sound is emitted.

In some embodiments, a playback device may repeatedly emit thecalibration sound during the calibration procedure such that thecalibration sound covers the calibration frequency range during eachrepetition. With a moving microphone, repetitions of the calibrationsound are continuously detected at different physical locations withinthe environment. For instance, the playback device might emit a periodiccalibration sound. Each period of the calibration sound may be detectedby the control device at a different physical location within theenvironment thereby providing a sample at that location. Such acalibration sound may therefore facilitate a space-averaged calibrationof the environment.

In some cases, the response of the one or more playback devices may beaveraged over time and/or space. For instance, the media playback systemmay determine respective responses from two or more of the repetitionsof the calibration sound and average these responses to determine atime-averaged response. Further, given a moving microphone, suchresponses may be further averaged across the listening area. A time-and/or space-averaged response may provide a better representation ofthe listening area as a whole. Further, such averaging may filter outtemporary conditions, such that the response indicates impairmentsaffect the calibration as a whole.

In some circumstances, multiple playback devices may be calibratedduring a calibration procedure. For instance, an example calibrationprocedure may calibrate a grouping of playback devices. Such a groupingmight be a zone of a media playback system that includes multipleplayback devices, or, perhaps a grouping might be formed from multiplezones of a media playback system that are grouped into a zone group thatincludes a respective playback device from each zone. Such groupingsmight be physically located within the same environment (e.g., a room ofa house or other building). In such cases, the control device maycapture respective calibration sounds from each of the playback devices,or a response indicating the group as a whole.

b. Determine Input Vector

At block 1004, the implementation 1000 involves determining an inputvector. For instance, a media playback system, such as media playbacksystem 100 of FIG. 1, may determine an input vector representing theresponse of the one or more playback devices. This input vector may beultimately provided to a neural network.

Determining the input vector may involve projecting a response vectoronto a principle component matrix representing variance caused by theknown impairments or conditions. As noted above, the response of the oneor more playback devices may be represented as a response vector. Asdescribed above in connection with block 606 of FIG. 6, theprinciple-component matrix represent a given set of the principlecomponents of a response matrix generated in the process of training theneural network. After training the network, this principle componentmatrix may be stored on one or more instances of a computing systemand/or distributed to media playback systems.

Some embodiments may further include converting the input vector or theresponse measurements from linear spacing to log-spacing in thefrequency domain, which may improve computational efficiency by reducingthe number of frequency bins (i.e., components) in the input matrix.However, such conversion might not be required in all embodiments.

In some cases, characteristic responses of devices used in training theneural network may affect detecting impairments. To offset suchcharacteristic responses, the media playback system may compensate forthe characteristic responses by removing the characteristic responsesfrom the input vector or the response vector. To remove thecharacteristic responses from the response measurements, the mediaplayback system may determine the difference in the frequency responseof the devices used in calibration relative to the device used intraining the matrix. The media playback system may then multiply (e.g.,take the dot product) of this difference and the response vector,thereby yielding a response vector that is not influenced by thecharacteristic responses of the devices used to train the neuralnetwork.

c. Provide Input Vector To Neural Network

In FIG. 10, at block 1006, implementation 1000 involves providing theinput vector to a neural network. As noted above, some neural networks(e.g., any of the neural networks discussed above in connection withimplementation 600, such as neural network 900) may be configured toaccept a vector as input (e.g., a vector p).

Within examples, the media playback system may provide the input vectorto a locally or remotely hosted neural network. For instance, the mediaplayback system may send the determined input vector to a computingsystem (e.g., an instance of computing system 500) along with a requestto provide the input vector to a particular neural network that has beentrained to detect a set of known impairments or conditions that mightaffect calibration and/or operation of the media playback system (e.g.,any of the neural networks discussed above in connection withimplementation 600, such as neural network 900). Alternatively, aninstance of the neural network may be stored locally on a device of themedia playback system and the media playback system may provide thedetermined input vector to that device.

d. Detect That The Input Vector Caused One Or More Neurons To Fire

Returning to FIG. 10, at block 1008, the implementation 1000 involvesdetecting that the input vector caused one or more neurons of the neuralnetwork to fire. For instance, one or more particular output neurons ofthe neural network may output respective values corresponding to“firing.” As noted above, a neuron may be considered to have “fired”when the output of its transfer function exceeds a firing threshold. Forinstance, a neural network may have a transfer function that outputsvalues between 0 and 1. Values about a certain level (e.g., .5 or .9)may be considered to have “fired” the neuron.

As described above in connection with block 610 of FIG. 6, in someexamples, the neural network includes at least one first neuroncorresponding to an absence of calibration impairment and one or moresecond neurons corresponding to respective calibration impairments. Insuch cases, detecting that the input matrix caused one or more neuronsof the neural network to fire may involve determining that thedetermined input vector did not cause the at least one first neuron tofire and determining that the input vector caused at least one of theone or more second neurons to fire. For instance, after determining thatinput vector did not cause the at least one first neuron to fire suchthat the neural network did not indicate a lack of impairments, therespective outputs of the one or more second neurons may be consideredto determine whether any of the second neurons fired. A second neuronfiring may indicate the presence of a particular impairment or conditioncorresponding to that neuron, as that second neuron was trained todetect that particular impairment or condition.

Certain known impairments that a neural network may be trained to detectmay include a case over the recording device (e.g., a protective caseover a smartphone). Such cases may cover or otherwise affect theoperation of a microphone. Certain types of cases may impair themicrophone in predictable ways (e.g., by consistently cutting and/orboosting certain frequencies). For instance, OTTERBOX® and/or LIFEPROOF®cases may provide a level of water resistance to the microphone of adevice by covering its microphone with flap made of rubber or some othermaterial. A flap of a particular type of case may impair detection ofaudio by the neural network in a predictable way that can be offset. Assuch, in some embodiments, one or more neurons may correspond toparticular types of recording-device cases. In such embodiments,detecting that the input vector caused one or more neurons of the neuralnetwork to fire may involve detecting that the input vector caused aneuron corresponding to a particular type of recording-device case tofire.

Neurons may correspond to any of the example known conditions orimpairments described herein, as well as other conditions that mightaffect operation of a media playback system. For instance, in some casesone or more neurons may correspond to different types of rooms (e.g.,kitchen, bathroom, bedroom, office) which may have characteristicresponses based on the furnishings and finishes typically found in suchroom types. As such, detecting that the input vector caused one or moreneurons of the neural network to fire may involve detecting that theinput vector caused a neuron corresponding to a particular type of roomto fire. While certain embodiments have been described herein, suchembodiments are not intended to be limiting, but rather exemplary ofpossible conditions or impairments that could be detected by disclosedtechniques.

e. Adjust Calibration Based On Detected Calibration Impairments OrConditions

In FIG. 10, at block 1010, implementation 1000 involves adjusting acalibration of the one or more playback devices to offset the one ormore particular calibration impairments. For example, as noted above,after detecting an impairment, some embodiments may further include oneor more of (a) indicating to a user visually (via a control device orplayback device), or audibly (via controller or audio playback device),that an impairment has been detected by visual or other means and/or (b)correcting the calibration to offset the detected impairment. If noimpairment is detected (e.g., an neuron corresponding to a lack ofimpairment fires or no neurons that correspond to known impairmentsfire), the media playback system may receive an indication of thisstatus and proceed with calibration and/or operation.

In some embodiments, the action taken may vary depending on the type ofimpairment detected. For example, for impairments that can be corrected,the techniques disclosed herein may correct or compensate for theimpairment. For other impairments (e.g., impairments that cannot bereadily offset), a control device or playback device may provide anotification (via a visible, audible, physical (e.g., vibration), orother notification) of the detected (or suspected) impairment so thatthe user can take action to correct or remove the impairment. Thenotification may also include instructions or suggestions for correctingthe detected (or suspected) impairment.

To illustrate, FIG. 11 shows an example playback device calibration userinterface 1100. As shown, the user interface 1100 includes a graphicalrepresentation 1102 indicating that one or more playback devices in a“LIVING ROOM” zone is being calibrated. Referring to FIG. 1, suchplayback devices may include playback devices 104, 106, 108, and/or 110.The user interface 1100 further includes a graphical representation 1104that may indicate that detecting of an audio signal for calibrationpurposes is taking place. The graphical representation 1104 may alsoshow a status of the audio signal recording process, such as an amountof the predetermined duration of time for detecting of the calibrationtone that has elapsed and/or that is remaining. The graphicalrepresentation 1104 may also show a representation of the audio signalthat has been detected thus far. Also shown in the user interface 1100is a selectable icon 1106 that may be selected to terminate thecalibration process. One having ordinary skill in the art willappreciate that the user interface 1100 of FIG. 6 is for illustrationpurposes and that other examples are also possible.

FIG. 12 shows an illustrative playback device calibration errorcondition user interface 1200 that may be displayed on the graphicalinterface when motion has not been validated. As shown, the userinterface 1200 includes a graphical representation 1202 indicating thatthe displayed content on the interface 1200 corresponds to the one ormore playback devices in the LIVING ROOM zone.

The user interface 1200 further includes a graphical representation 1210that may include a message describing detected known impairments orconditions and/or a prompt to remedy the detected impairments orconditions. The user interface 1200 further includes selectable icons1206 and 1208. Selectable icon 1506 may be selected to try thecalibration process again, and selectable icon 1208 may be selected toterminate the calibration process. As shown, the graphicalrepresentation 1210 may overlay a grayed or dimmed version of some orall of the graphical representation 604 of the user interface 600 ofFIG. 6.

In one example, each type of known impairment or condition may have acorresponding textual message to be provided in the graphicalrepresentation 1510. For instance, if the identified impairmentcorresponds to flint covering the microphone, an example textual messagemay be “To get a good measurement, make sure your microphone isunobstructed by lint and other material.” In another instance, if theidentified impairment corresponds to a finger obstructing themicrophone, an example textual message may be “It sounds like you werekeeping a finger over the microphone. Please try again, but watch thosefingers.”

The example textual messages discussed herein are for illustrativepurposes only and are not meant to be limiting. Further, one havingordinary skill in the art will appreciate that other examples are alsopossible.

As noted above, some known impairments may affect operation of arecording device or a playback device in predictable ways. In somecases, responses that are characteristic of such impairments may bedetermined (e.g., when training the neural network). Such responses maybe used to correct for the known impairment to allow calibration orother operation of the media playback system to proceed withoutnecessarily removing the impairment. In particular, such responses maybe used to offset the impairment.

For instance, after the playback device emits the calibration soundduring the calibration interval, the recordings of the calibrationsounds may be analyzed to determine calibration settings for theplayback device. Such analysis may involve determining a response of theplayback device in a given listening area, and determining a calibrationprofile to offset characteristics of the environment on the apparentresponse of the playback device. Inverses of such characteristicresponses may be applied to the response of the playback device in thegiven listening area, so as to offset the effect of the impairment onthe response measurement.

Some examples techniques for analyzing such recordings are described inU.S. patent application Ser. No. 13/536,493 filed Jun. 28, 2012,entitled “System and Method for Device Playback Calibration,” U.S.patent application Ser. No. 14/216,306 filed Mar. 17, 2014, entitled“Audio Settings Based On Environment,” and U.S. patent application Ser.No. 14/481,511 filed Sep. 9, 2014, entitled “Playback DeviceCalibration,” which are incorporated herein in their entirety.

VI. Conclusion

The description above discloses, among other things, various examplesystems, methods, apparatus, and articles of manufacture including,among other components, firmware and/or software executed on hardware.It is understood that such examples are merely illustrative and shouldnot be considered as limiting. For example, it is contemplated that anyor all of the firmware, hardware, and/or software aspects or componentscan be embodied exclusively in hardware, exclusively in software,exclusively in firmware, or in any combination of hardware, software,and/or firmware. Accordingly, the examples provided are not the onlyway(s) to implement such systems, methods, apparatus, and/or articles ofmanufacture.

Additionally, references herein to “embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment can be included in at least one example embodiment of aninvention. The appearances of this phrase in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. As such, the embodiments described herein, explicitly andimplicitly understood by one skilled in the art, can be combined withother embodiments.

The specification is presented largely in terms of illustrativeenvironments, systems, procedures, steps, logic blocks, processing, andother symbolic representations that directly or indirectly resemble theoperations of data processing devices coupled to networks. These processdescriptions and representations are typically used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art. Numerous specific details are set forth to provide athorough understanding of the present disclosure. However, it isunderstood to those skilled in the art that certain embodiments of thepresent disclosure can be practiced without certain, specific details.In other instances, well known methods, procedures, components, andcircuitry have not been described in detail to avoid unnecessarilyobscuring aspects of the embodiments. Accordingly, the scope of thepresent disclosure is defined by the appended claims rather than theforgoing description of embodiments.

When any of the appended claims are read to cover a purely softwareand/or firmware implementation, at least one of the elements in at leastone example is hereby expressly defined to include a tangible,non-transitory medium such as a memory, DVD, CD, Blu-ray, and so on,storing the software and/or firmware.

(Feature 1) A method configured for receiving data indicating a responseof one or more playback devices captured by a given microphone,determining an input vector by projecting a response vector thatrepresents the response of the one or more playback devices onto aprinciple component matrix representing variance caused by one or morecalibration impairments, providing the determined input vector to aneural network that includes an output layer comprising neurons thatcorrespond to respective conditions, detecting that the input vectorcaused one or more neurons of the neural network to fire such that theneural network indicates that one or more particular conditions areaffecting the microphone and/or the playback device, and adjustingoperation of the one or more playback devices based on the one or moreparticular conditions.

(Feature 2) The method of feature 1, wherein the conditions comprisecalibration impairments and wherein adjusting operation of the one ormore playback devices based on the one or more particular conditionscomprises to adjusting calibration of the one or more playback devicesoffset the one or more particular calibration impairments.

(Feature 3) The method of feature 1 in combination with feature 2,wherein adjusting the calibration of the one or more playback devices tooffset the one or more particular calibration impairments comprisessending a message that causes the one or more playback devices to abortthe calibration.

(Feature 4) The method of feature 1 or 3 in combination with feature 2,wherein adjusting the calibration of the one or more playback devices tooffset the one or more particular calibration impairments comprisesdisplaying a prompt to remove the calibration impairments from themicrophone.

(Feature 5) The method of feature 1, 3, or 4 in combination with feature2, wherein adjusting the calibration of the one or more playback devicesto offset the one or more particular calibration impairments comprisesapplying one or more respective correction curves corresponding to theone or more particular calibration impairments to a calibration profilethat offsets acoustic characteristics of the given environment tocalibrate the one or more playback devices to a calibrationequalization.

(Feature 6) The method of feature 1 or any of features 3-5 incombination with feature 2, wherein the neural network includes at leastone first neuron corresponding to an absence of calibration impairmentand one or more second neurons corresponding to respective calibrationimpairments, and wherein detecting that the input matrix caused one ormore neurons of the neural network to fire comprises determining thatthe determined input vector did not cause the at least one first neuronto fire, and determining that the input vector caused at least one ofthe one or more second neurons to fire.

(Feature 7) The method of feature 1 or any of features 3-6 incombination with feature 2, wherein detecting that the input vectorcaused one or more neurons of the neural network to fire comprisesdetermining that output value of neuron transfer functions correspondingto respective neurons exceed a firing threshold.

(Feature 8) The method of feature 1 or any of features 3-7 incombination with feature 2, wherein detecting that the input vectorcaused one or more neurons of the neural network to fire comprisesdetecting that the input vector caused a neuron corresponding to aparticular type of recording-device case to fire.

(Feature 9) The method of feature 1 or any of features 3-8 incombination with feature 2, wherein detecting that the input vectorcaused one or more neurons of the neural network to fire comprisesdetecting that the input vector caused a neuron corresponding to aparticular type of room to fire.

(Feature 10) The method of feature 1 or any of features 3-9 incombination with feature 2, wherein determining the input vectorrepresenting the response of the microphone in the given environmentcomprises identifying a subset of the received data indicating theresponse of one or more playback devices, the subset corresponding tofrequencies affected by the microphone impairments, and determining anresponse vector that includes components representing the identifiedsubset.

(Feature 11) The method of feature 1 or any of features 3-10 incombination with feature 2, wherein determining the input vector thatincludes components representing the identified subset comprisesconverting the received data indicating the response of one or moreplayback devices from linear spacing to logarithmic spacing, anddetermining a response vector that includes log-spaced componentsrepresenting the identified subset.

(Feature 12) The method of feature 1 or any of features 3-11 incombination with feature 2, wherein receiving data indicating theresponse of the one or more playback devices comprises receiving dataindicating a response of one or more first playback devices of a firsttype in a given environment, and wherein the network device is furtherconfigured for determining a difference between the response of the oneor more first playback devices and a response corresponding to a secondtype of playback device, wherein the neural network was trained with asecond playback device of the second type, and determining the responsevector by determining a product of an inverse of the determineddifference and the response of the one or more first playback devices soas to offset type of playback device.

(Feature 13) The method of feature 1 or any of features 3-12 incombination with feature 2, wherein adjusting operation of the playbackdevice to offset the one or more particular conditions comprisesinterrupting calibration of the playback device, while calibration isinterrupted, causing a control device to display a prompt to remove theconditions, and upon receiving an indication that the conditions areremoved, continuing calibration of the playback device.

(Feature 14) A network device configured for performing the method ofany of features 1 to 13.

(Feature 15) A computer readable medium for performing the method of anyof features 10 to 17.

(Feature 16) A method of training a neural network to identifyimpairments, the method comprising receiving a response matrix thatrepresents, in respective dimensions, responses of a given playbackdevice under respective iterations of a sound captured by a recordingdevice, the iterations including first iterations with respectiveimpairments to the recording device and second iterations without therespective impairments to the recording device, determining principlecomponents representing axes of greatest variance in the responsematrix, the principle components comprising respective eigenvectors thatinclude a component for each of the respective iterations, determining aprinciple-component matrix that represents a given set of the principlecomponents, determining a teaching matrix by projecting theprinciple-component onto the response matrix, and training a neuralnetwork that includes an output layer comprising a neuron for each ofthe respective impairments by iteratively providing vectors of thetraining matrix to the neural network.

(Feature 17) The method of claim 16, further comprising storing thetrained neural network on a computing system.

(Feature 18) The method of claim 16 or claim 17, wherein receiving theresponse matrix that represents, in respective dimensions, responses ofthe given playback device under respective iterations comprisesreceiving data indicating response vectors for each of the respectiveiterations and combining the vectors into the response matrix thatincludes a dimension for each iteration.

(Feature 19) The method of any of claims 16-18, wherein training theneural network comprises determining error between the teaching matrixand output of the neural network, and adjusting respective transferfunction factors of the neurons to offset the determined errors.

(Feature 20) The method of any of claims 16-19, wherein the givenplayback device is a first type of playback device, the method furthercomprising determining a difference between the response of the givenplayback device and a response corresponding to a second type ofplayback device, and determining the response matrix by determining aproduct of an inverse of the determined difference and the response ofthe second type of playback device so as to offset type of playbackdevice.

(Feature 21) A network device configured for performing the method ofany of features 16 to 20.

(Feature 22) A computing system configured for performing the method ofany of features 16 to 20.

(Feature 23) A non-transitory computer readable medium for performingthe method of any of features 16 to 20.

We claim:
 1. Tangible, non-transitory computer-readable medium havingstored thereon instructions executable by a computing device to performfunctions comprising: receiving, by the computing device, a responsematrix that represents, in respective dimensions, responses of a givenplayback device under respective iterations of a sound captured by arecording device, the iterations including first iterations withrespective impairments to the recording device and second iterationswithout the respective impairments to the recording device; determining,by the computing device, principle components representing the axes ofgreatest variance in the response matrix, the principle componentscomprising respective eigenvectors that include a component for each ofthe respective iterations; determining a principle-component matrix thatrepresents a given set of the principle components; determining ateaching matrix by projecting the principle-component onto the responsematrix; training a neural network that includes an output layercomprising a neuron for each of the respective impairments byiteratively providing vectors of the teaching matrix to the neuralnetwork, wherein training the neural network comprises: determiningerror between the teaching matrix and output of the neural network; andadjusting respective transfer function factors of the neurons to offsetthe determined error; and storing the trained neural network on acomputing system.
 2. The tangible, non-transitory computer-readablemedium of claim 1, wherein receiving the response matrix thatrepresents, in respective dimensions, responses of the given playbackdevice under respective iterations comprises: receiving data indicatingvectors for each of the respective iterations; and combining the vectorsinto the response matrix that includes a dimension for each iteration.3. The tangible, non-transitory computer-readable medium of claim 1,wherein the given playback device is a first type of playback device,and wherein the functions further comprise: determining a differencebetween the response of the given playback device and a responsecorresponding to a second type of playback device; and determining theresponse matrix by determining a product of an inverse of the determineddifference and the response of the second type of playback device tooffset the different types of playback devices.
 4. The tangible,non-transitory computer-readable medium of claim 1, wherein thefunctions further comprise: receiving data indicating a response of oneor more playback devices captured by a given microphone; determining aninput vector by projecting a response vector that represents theresponse of the one or more playback devices onto a principle componentmatrix representing variance caused by one or more calibrationimpairments; providing the determined input vector to the trained neuralnetwork; detecting that the input vector caused one or more neurons ofthe neural network to fire such that the trained neural networkindicates that one or more particular calibration impairments areaffecting the given microphone; and causing a calibration of the one ormore playback devices to be adjusted to offset the one or moreparticular calibration impairments.
 5. The tangible, non-transitorycomputer-readable medium of claim 4, wherein causing the calibration ofthe one or more playback devices to be adjusted to offset the one ormore particular calibration impairments comprises: applying one or morerespective correction curves corresponding to the one or more particularcalibration impairments to a calibration profile that offsets acousticcharacteristics of a given environment to calibrate the one or moreplayback devices to a calibration equalization.
 6. The tangible,non-transitory computer-readable medium of claim 4, wherein detectingthat the input vector caused one or more neurons of the trained neuralnetwork to fire comprises detecting that the input vector caused aneuron corresponding to a particular type of recording-device case tofire.
 7. A computing device comprising: one or more processors; andtangible, non-transitory computer-readable medium having stored thereoninstructions executable by the one or more processors to cause thecomputing device to perform functions comprising: receiving, by thecomputing device, a response matrix that represents, in respectivedimensions, responses of a given playback device under respectiveiterations of a sound captured by a recording device, the iterationsincluding first iterations with respective impairments to the recordingdevice and second iterations without the respective impairments to therecording device; determining, by the computing device, principlecomponents representing the axes of greatest variance in the responsematrix, the principle components comprising respective eigenvectors thatinclude a component for each of the respective iterations; determining aprinciple-component matrix that represents a given set of the principlecomponents; determining a teaching matrix by projecting theprinciple-component onto the response matrix; training a neural networkthat includes an output layer comprising a neuron for each of therespective impairments by iteratively providing vectors of the teachingmatrix to the neural network, wherein training the neural networkcomprises: determining error between the teaching matrix and output ofthe neural network; and adjusting respective transfer function factorsof the neurons to offset the determined error; and storing the trainedneural network on a computing system.
 8. The computing device of claim7, wherein receiving the response matrix that represents, in respectivedimensions, responses of the given playback device under respectiveiterations comprises: receiving data indicating vectors for each of therespective iterations; and combining the vectors into the responsematrix that includes a dimension for each iteration.
 9. The computingdevice of claim 7, wherein the given playback device is a first type ofplayback device, wherein the functions further comprise: determining adifference between the response of the given playback device and aresponse corresponding to a second type of playback device determiningthe response matrix by determining a product of an inverse of thedetermined difference and the response of the second type of playbackdevice to offset the different types of playback devices.
 10. Thecomputing device of claim 7, wherein the functions further comprise:receiving data indicating a response of one or more playback devicescaptured by a given microphone; determining an input vector byprojecting a response vector that represents the response of the one ormore playback devices onto a principle component matrix representingvariance caused by one or more calibration impairments; providing thedetermined input vector to the trained neural network; detecting thatthe input vector caused one or more neurons of the neural network tofire such that the trained neural network indicates that one or moreparticular calibration impairments are affecting the given microphone;and causing a calibration of the one or more playback devices to beadjusted to offset the one or more particular calibration impairments.11. The computing device of claim 10, wherein causing the calibration ofthe one or more playback devices to be adjusted to offset the one ormore particular calibration impairments comprises: transmitting to anetwork device corresponding to the given microphone, data indicatingthe one or more particular calibration impairments.
 12. The computingdevice of claim 10, wherein detecting that the input vector caused oneor more neurons of the trained neural network to fire comprisesdetecting that the input vector caused a neuron corresponding to aparticular type of room to fire.
 13. The computing device of claim 10,wherein detecting that the input vector caused one or more neurons ofthe trained neural network to fire comprises detecting that the inputvector caused a neuron corresponding to a particular type ofrecording-device case to fire.
 14. A method of training a neural networkto identify impairments, the method comprising: receiving, by acomputing device, a response matrix that represents, in respectivedimensions, responses of a given playback device under respectiveiterations of a sound captured by a recording device, the iterationsincluding first iterations with respective impairments to the recordingdevice and second iterations without the respective impairments to therecording device; determining, by the computing device, principlecomponents representing the axes of greatest variance in the responsematrix, the principle components comprising respective eigenvectors thatinclude a component for each of the respective iterations; determining aprinciple-component matrix that represents a given set of the principlecomponents; determining a teaching matrix by projecting theprinciple-component onto the response matrix; training a neural networkthat includes an output layer comprising a neuron for each of therespective impairments by iteratively providing vectors of the teachingmatrix to the neural network, wherein training the neural networkcomprises: determining error between the teaching matrix and output ofthe neural network; and adjusting respective transfer function factorsof the neurons to offset the determined error; and storing the trainedneural network on a computing system.
 15. The method of claim 14,wherein receiving the response matrix that represents, in respectivedimensions, responses of the given playback device under respectiveiterations comprises: receiving data indicating vectors for each of therespective iterations; and combining the vectors into the responsematrix that includes a dimension for each iteration.
 16. The method ofclaim 14, wherein the given playback device is a first type of playbackdevice, the method further comprising: determining a difference betweenthe response of the given playback device and a response correspondingto a second type of playback device determining the response matrix bydetermining a product of an inverse of the determined difference and theresponse of the second type of playback device so as to offset thedifferent types of playback devices.
 17. The method of claim 14, furthercomprising: receiving data indicating a response of one or more playbackdevices captured by a given microphone; determining an input vector byprojecting a response vector that represents the response of the one ormore playback devices onto a principle component matrix representingvariance caused by one or more calibration impairments; providing thedetermined input vector to the trained neural network; detecting thatthe input vector caused one or more neurons of the neural network tofire such that the trained neural network indicates that one or moreparticular calibration impairments are affecting the given microphone;and causing a calibration of the one or more playback devices to beadjusted to offset the one or more particular calibration impairments.18. The method of claim 17, wherein detecting that the input vectorcaused one or more neurons of the trained neural network to firecomprises determining that output values of neuron transfer functionscorresponding to respective neurons exceed a firing threshold.
 19. Themethod of claim 17, wherein detecting that the input vector caused oneor more neurons of the trained neural network to fire comprisesdetecting that the input vector caused a neuron corresponding to aparticular type of recording-device case to fire.
 20. The method ofclaim 17, wherein detecting that the input vector caused one or moreneurons of the trained neural network to fire comprises detecting thatthe input vector caused a neuron corresponding to a particular type ofroom to fire.